I. Field of the Invention
The present invention relates generally to the fields of biology and cellular image processing. In certain aspects, the invention is related to methods for cell segmentation and cellular image processing.
II. Description of Related Art
Studying the migration and proliferation behavior of cells contributes to the understanding of biological processes and disease pathologies such as cancer, angiogenesis, vascular stenosis and arthritis. To this end, cellular imaging coupled with image processing and segmentation techniques, has been used to provide an automatic and improved analysis of the cellular behavior.
Cell segmentation is one of the many challenging tasks in cellular image processing. Several methods have been proposed and developed for cell segmentation and tracking Kachouie and Fieguth (2005); Debeir et al. (2005); Espinoza et al. (2006); Bunyak et al. (2006); Li et al. (2006); Yang et al. (2005); Kachouie et al. (2005). In general, these methods work for specific cell types and under specific constraints. In Kachouie and Fieguth (2005), a gradient-based level-set method is used for cluster segmentation of neural stem cells. But the method of Kachouie and Fieguth (2005) is not robust in the presence of noise. Moreover, this method is not fully automatic as it requires the knowledge of the cell cluster location relative to the initial boundary of the evolving level-set function. In Debeir et al. (2005), a cell tracking scheme is presented, for in vitro phase contrast video microscopy, using a combination of mean shift processes. However, in the presented method Debeir et al. (2005), the user needs to manually select the locations of the cells in the first or the last frame of the video sequence. In addition, the original frames need to be pre-processed by performing contrast enhancement and illumination correction. In Espinoza et al. (2006), a cell cluster segmentation algorithm is presented based on global and local thresholding for In-SITU microscopic images. This method requires noise-free images, non-overlapping cells, and a high-contrast between cells and background. Also, many parameters need to be adjusted to compute the local threshold. In Bunyak et al. (2006), the level-set method of Vese and Chan (2002) is used, with one level-set function, for the segmentation and tracking of multiple motile epithelial cells during wound healing. In Li et al. (2006), a topology-constrained level-set method is presented to prevent the merging of touching and partially overlapping cells. Level-set methods were also used for cell segmentation and tracking in Yang et al. (2005). In Kachouie et al. (2005), a probabilistic model was proposed for the segmentation of hematopoietic stem cells; the proposed model is based on identifying the most probable cell locations in the image on the basis of cell brightness and morphology. The latter method is sensitive to cell overlap, cell shape, and the used threshold. Moreover, the methods of Li et al. (2006); Yang et al. (2005); Kachouie et al. (2005) require noise-free images with a good contrast between the cells and the background.
Asaad et al. (2007) presented a cell migration and cell counting scheme for bladder cancer cell images, where the segmentation is based on a piecewise level-set segmentation method. The goal was to automatically determine the overall cell migration rate, from a set of acquired bladder cancer cell images, by extracting and analyzing the evolution of the region where the cells cluster. This “cell cluster” region is denoted as the region of interest (ROI). A mathematical morphology process was used as the first step in the scheme of Asaad et al. (2007) to smooth and fill the gaps between individual cells inside the ROI. This step was needed before applying the piecewise level-set segmentation method. In the piecewise level-set segmentation methods, objects can be separated from the background based on different mean values of object and background. In some cases, these mean values are very close to each other, which causes the piecewise segmentation to fail in capturing the object (ROI). Moreover, if the image has artifacts, such as the ones shown in FIG. 4, FIG. 6, and FIG. 7 between the ROI and outer-circle, the piecewise method will segment the artifact area because it has a different mean than the background.
Lee et al. disclose a method for adaptive image region partition and morphologic processing in U.S. Pub. 2008/0037872. This method provides for partitioning an image into zones of influence (ZOI). However, this method is only applicable to high resolution images, and therefore has limited usefulness in cellular imaging.
Rimm et al. disclose a method for automated analysis of cells and tissues in U.S. Pub. 2008/0046190. This method provides for rapidly analyzing cell-containing samples, and employs biomarkers and stains to identify and analyze, for example, the location of subcellular compartments within individual cells. However, this method is also only applicable to high resolution images, and the samples must be prepared prior to the analysis of the image.
Bocking et al. disclose in U.S. Pub. 2008/0044849 a method to analyze cells, wherein the cells are adhesively applied to a slide and stained. In particular, the method includes treating the cells in any manner that alters the emission, transmission, and/or absorption behavior of the cells in relation to electromagnetic waves. Thus, this method involves the need to alter the sample prior to obtaining the image.
Vaisberg et al. (2006) disclose in U.S. Pat. No. 7,151,847 a method, code, and apparatus for analyzing cell images in order to automatically identify and characterize the Golgi complex in individual cells. The disclosed method, code, and apparatus of Vaisberg et al. does not deal with the segmentation of clusters of cells, but with identifying the Golgi complex within individual cells, which is not related to cell migration analysis.
There is therefore a need for a method for the automatic segmentation and analysis of the overall migration rate and proliferation rate of cells, that may be robust to noise and artifacts and that may be used with any resolution image and without the need to prepare the sample or the image before the image is analyzed. One way to overcome the disadvantages of the prior art is to consider the ROI area (cell cluster) as a texture region. This is based on the observation that the ROI region of the considered cancer cells has a high variance and a texture-like structure, such as grain, sands, and other textures, as compared to the other present artifacts and non-cell image features.